202 lines
6.7 KiB
Markdown
202 lines
6.7 KiB
Markdown
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# Text-to-SQL
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[[open-in-colab]]
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在此教程中，我们将看到如何使用 `smolagents` 实现一个利用 SQL 的 agent。
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> 让我们从经典问题开始：为什么不简单地使用标准的 text-to-SQL pipeline 呢？
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标准的 text-to-SQL pipeline 很脆弱，因为生成的 SQL 查询可能会出错。更糟糕的是，查询可能出错却不引发错误警报，从而返回一些不正确或无用的结果。
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👉 相反，agent 系统则可以检视输出结果并决定查询是否需要被更改，因此带来巨大的性能提升。
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让我们来一起构建这个 agent! 💪
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首先，我们构建一个 SQL 的环境：
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```py
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from sqlalchemy import (
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create_engine,
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MetaData,
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Table,
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Column,
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String,
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Integer,
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Float,
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insert,
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inspect,
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text,
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)
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engine = create_engine("sqlite:///:memory:")
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metadata_obj = MetaData()
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# create city SQL table
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table_name = "receipts"
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receipts = Table(
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table_name,
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metadata_obj,
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Column("receipt_id", Integer, primary_key=True),
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Column("customer_name", String(16), primary_key=True),
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Column("price", Float),
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Column("tip", Float),
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)
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metadata_obj.create_all(engine)
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rows = [
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{"receipt_id": 1, "customer_name": "Alan Payne", "price": 12.06, "tip": 1.20},
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{"receipt_id": 2, "customer_name": "Alex Mason", "price": 23.86, "tip": 0.24},
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{"receipt_id": 3, "customer_name": "Woodrow Wilson", "price": 53.43, "tip": 5.43},
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{"receipt_id": 4, "customer_name": "Margaret James", "price": 21.11, "tip": 1.00},
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]
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for row in rows:
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stmt = insert(receipts).values(**row)
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with engine.begin() as connection:
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cursor = connection.execute(stmt)
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```
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### 构建 agent
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现在，我们构建一个 agent，它将使用 SQL 查询来回答问题。工具的 description 属性将被 agent 系统嵌入到 LLM 的提示中：它为 LLM 提供有关如何使用该工具的信息。这正是我们描述 SQL 表的地方。
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```py
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inspector = inspect(engine)
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columns_info = [(col["name"], col["type"]) for col in inspector.get_columns("receipts")]
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table_description = "Columns:\n" + "\n".join([f" - {name}: {col_type}" for name, col_type in columns_info])
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print(table_description)
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```
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```text
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Columns:
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- receipt_id: INTEGER
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- customer_name: VARCHAR(16)
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- price: FLOAT
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- tip: FLOAT
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```
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现在让我们构建我们的工具。它需要以下内容：（更多细节请参阅[工具文档](../tutorials/tools)）
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- 一个带有 `Args:` 部分列出参数的 docstring。
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- 输入和输出的type hints。
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```py
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from smolagents import tool
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@tool
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def sql_engine(query: str) -> str:
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"""
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Allows you to perform SQL queries on the table. Returns a string representation of the result.
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The table is named 'receipts'. Its description is as follows:
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Columns:
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- receipt_id: INTEGER
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- customer_name: VARCHAR(16)
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- price: FLOAT
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- tip: FLOAT
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Args:
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query: The query to perform. This should be correct SQL.
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"""
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output = ""
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with engine.connect() as con:
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rows = con.execute(text(query))
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for row in rows:
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output += "\n" + str(row)
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return output
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```
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我们现在使用这个工具来创建一个 agent。我们使用 `CodeAgent`，这是 smolagent 的主要 agent 类：一个在代码中编写操作并根据 ReAct 框架迭代先前输出的 agent。
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这个模型是驱动 agent 系统的 LLM。`HfApiModel` 允许你使用 HF Inference API 调用 LLM，无论是通过 Serverless 还是 Dedicated endpoint，但你也可以使用任何专有 API。
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```py
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from smolagents import CodeAgent, HfApiModel
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agent = CodeAgent(
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tools=[sql_engine],
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model=HfApiModel("meta-llama/Meta-Llama-3.1-8B-Instruct"),
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)
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agent.run("Can you give me the name of the client who got the most expensive receipt?")
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```
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### Level 2: 表连接
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现在让我们增加一些挑战！我们希望我们的 agent 能够处理跨多个表的连接。因此，我们创建一个新表，记录每个 receipt_id 的服务员名字！
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```py
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table_name = "waiters"
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receipts = Table(
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table_name,
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metadata_obj,
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Column("receipt_id", Integer, primary_key=True),
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Column("waiter_name", String(16), primary_key=True),
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)
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metadata_obj.create_all(engine)
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rows = [
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{"receipt_id": 1, "waiter_name": "Corey Johnson"},
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{"receipt_id": 2, "waiter_name": "Michael Watts"},
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{"receipt_id": 3, "waiter_name": "Michael Watts"},
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{"receipt_id": 4, "waiter_name": "Margaret James"},
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]
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for row in rows:
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stmt = insert(receipts).values(**row)
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with engine.begin() as connection:
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cursor = connection.execute(stmt)
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```
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因为我们改变了表，我们需要更新 `SQLExecutorTool`，让 LLM 能够正确利用这个表的信息。
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```py
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updated_description = """Allows you to perform SQL queries on the table. Beware that this tool's output is a string representation of the execution output.
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It can use the following tables:"""
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inspector = inspect(engine)
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for table in ["receipts", "waiters"]:
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columns_info = [(col["name"], col["type"]) for col in inspector.get_columns(table)]
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table_description = f"Table '{table}':\n"
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table_description += "Columns:\n" + "\n".join([f" - {name}: {col_type}" for name, col_type in columns_info])
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updated_description += "\n\n" + table_description
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print(updated_description)
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```
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因为这个request 比之前的要难一些，我们将 LLM 引擎切换到更强大的 [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct)！
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```py
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sql_engine.description = updated_description
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agent = CodeAgent(
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tools=[sql_engine],
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model=HfApiModel("Qwen/Qwen2.5-Coder-32B-Instruct"),
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)
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agent.run("Which waiter got more total money from tips?")
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```
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它直接就能工作！设置过程非常简单，难道不是吗？
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这个例子到此结束！我们涵盖了这些概念：
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- 构建新工具。
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- 更新工具的描述。
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- 切换到更强大的 LLM 有助于 agent 推理。
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✅ 现在你可以构建你一直梦寐以求的 text-to-SQL 系统了！✨
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